The term “nanoparticle stability” is widely used to describe the preservation of a particular nanostructure property ranging from aggregation, composition, crystallinity, shape, size, and surface chemistry. As a result, this catch-all term has various meanings, which depend on the specific nanoparticle property of interest and/or application. In this feature article, we provide an answer to the question, “What does nanoparticle stability mean?”. Broadly speaking, the definition of nanoparticle stability depends on the targeted size dependent property that is exploited and can only exist for a finite period of time given all nanostructures are inherently thermodynamically and energetically unfavorable relative to bulk states. To answer this question specifically, however, the relationship between nanoparticle stability and the physical/chemical properties of metal/metal oxide nanoparticles are discussed. Specific definitions are explored in terms of aggregation state, core composition, shape, size, and surface chemistry. Next, mechanisms of promoting nanoparticle stability are defined and related to these same nanoparticle properties. Metrics involving both kinetics and thermodynamics are considered. Methods that provide quantitative metrics for measuring and modeling nanoparticle stability in terms of core composition, shape, size, and surface chemistry are outlined. The stability of solution-phase nanoparticles are also impacted by aggregation state. Thus, collision and DLVO theories are discussed. Finally, challenges and opportunities in understanding what nanoparticle stability means are addressed to facilitate further studies with this important class of materials.

Unwanted nanoparticle aggregation and/or agglomeration may occur when anisotropic nanoparticles are dispersed in various solvents and matrices. While extended Derjaguin-Landau-Verwey-Overbeek (DLVO) theory has been successfully applied to predict nanoparticle stability in solution, this model fails to accurately predict the physical stability of anisotropic nanostructures; thus limiting its applicability in practice. Herein, DLVO theory was used to accurately predict gold nanostar stability in solution by investigating how the choice of the nanostar dimension considered in calculations influences the calculated attractive and repulsive interactions between nanostructures. The use of the average radius of curvature of the nanostar tips instead of the average radius as the nanostar dimension of interest increases the accuracy with which experimentally observed nanoparticle behavior can be modeled theoretically. This prediction was validated by measuring time-dependent localized surface plasmon resonance (LSPR) spectra of gold nanostars suspended in solutions with different ionic strengths. Minimum energy barriers calculated from collision theory as a function of nanoparticle concentration were utilized to make kinetic predictions. All in all, these studies suggest that choosing the appropriate gold nanostar dimension is crucial to fully understanding and accurately predicting the stability of anisotropic nanostructures such as gold nanostars; i.e., whether the nanostructures remain stable and can be used reproducibly, or whether they aggregate and exhibit inconsistent results. Thus, the present work provides a deeper understanding of internanoparticle interactions in solution and is expected to lead to more consistent and efficient analytical and bioanalytical applications of these important materials in the future. Graphical abstract ᅟ.

The behavior of prostate carcinoma (PC3) cells and human dermal fibroblast (HDF) cells when incubated with sedimented Au NPs in vitro is studied. Darkfield microscopy demonstrates that both PC3 and HDF cells can "vacuum" Au NPs from the surface. Mean square displacement and mean cumulative square distance of cells shows that PC3 migration decreases in the presence of Au NPs while for HDF, migration is dependent on the surface charge and shape of Au NPs.

Surface-enhanced Raman scattering (SERS) is a surface sensitive technique that reveals information regarding molecular adsorption driving forces at nanoparticles surfaces. While the plasmonic properties of SERS substrates provide the largest signal enhancements, chemical enhancement mechanisms are more sensitive to molecular adsorption and intermolecular interactions. Herein, gold coated silver nanoparticles that are stabilized inside microporous silica membranes are used for monitoring short-range chemical enhancement effects. First, the silica membrane provides plasmonic stability while also facilitating kinetic measurements so that impacts of molecular protonation, molecule–molecule interactions, molecule–silica interactions, and molecule–Au interactions can be identified. To do this, the vibrational frequencies of 4-mercaptobenzoic acid (4-MBA) are monitored as a function of time and pH. Applying Fick’s second law to time-dependent responses reveals that molecular flux decreases with increasing pH. SERS spectra suggest that the kinetics of this phenomenon depend on the protonation state of 4-MBA and, hence, the energy required for the molecules to pass through the negatively charged silica membrane. Namely, repulsive electrostatic interactions between deprotonated molecules (R-COO–) and the silica shell increase the energy required for transport, which subsequently decreases the flux of molecules through the silica shell and subsequent adsorption to the metal surface. As pH approaches neutral conditions, the fraction of deprotonated 4-MBA increases. These molecules, which have a higher electron density in the aromatic rings versus protonated ones, favor selective chemical enhancement of the asymmetric versus symmetric C–C stretching modes. In addition, increasing intermolecular interactions between adsorbed molecules promote electron delocalization from aromatic rings to the carboxylate groups of 4-MBA. This response causes the pK a of the carboxylate to gradually increase from 4.8 (in solution) to 7.7 (on nanoparticle surfaces). Consequently, SERS signals for this molecule can be understood with respect to molecular protonation state, flux, and intermolecular interactions using these electromagnetically stable plasmonic nanostructures.

Reproducible detection of uranyl, an important biological and environmental contaminant, from complex matrixes by surface-enhanced Raman scattering (SERS) is successfully achieved using amidoximated-polyacrylonitrile (AO-PAN) mats and carboxylated gold (Au) nanostars. SERS detection of small molecules from a sample mixture is traditionally limited by nonspecific adsorption of nontarget species to the metal nanostructures and subsequent variations in both the vibrational frequencies and intensities. Herein, this challenge is overcome using AO-PAN mats to extract uranyl from matrixes ranging in complexity including HEPES buffer, Ca(NO) and NaHCO solutions, and synthetic urine. Subsequently, Au nanostars functionalized with carboxyl-terminated alkanethiols are used to enhance the uranyl signal. The detected SERS signals scale with uranyl uptake as confirmed using liquid scintillation counting. SERS vibrational frequencies of uranyl on both hydrated and lyophilized polymer mats are largely independent of sample matrix, indicating less complexity in the uranyl species bound to the surface of the mats vs in solution. These results suggest that matrix effects, which commonly limit the use of SERS for complex sample analysis, are minimized for uranyl detection. The presented synergistic approach for isolating uranyl from complex sample matrixes and enhancing the signal using SERS is promising for real-world sample detection and eliminates the need of radioactive tracers and extensive sample pretreatment steps.